


"""simple docstring"""
import os
import warnings
from typing import List, Optional

from ...tokenization_utils_base import BatchEncoding
from ...utils import logging
from .configuration_rag import RagConfig


UpperCAmelCase				=						logging.get_logger(__name__)




class 	lowercase__				:





   def __init__(							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE)  ->  int:
      _lowerCamelCase	:					Any   =		question_encoder
      _lowerCamelCase	:					Optional[int]   =		generator
      _lowerCamelCase	:					Tuple   =		self.question_encoder





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE)  ->  Union[str, Any]:
      if os.path.isfile(SCREAMING_SNAKE_CASE):
         raise ValueError(F'Provided path ({save_directory}) should be a directory, not a file')
      os.makedirs(SCREAMING_SNAKE_CASE      ,   exist_ok=SCREAMING_SNAKE_CASE)
      _lowerCamelCase	:					int   =		os.path.join(SCREAMING_SNAKE_CASE      ,   """question_encoder_tokenizer""")
      _lowerCamelCase	:					Any   =		os.path.join(SCREAMING_SNAKE_CASE      ,   """generator_tokenizer""")
      self.question_encoder.save_pretrained(SCREAMING_SNAKE_CASE)
      self.generator.save_pretrained(SCREAMING_SNAKE_CASE)





   @classmethod
   def 		UpperCamelCase_  (							cls      ,   SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  Optional[int]:
      # dynamically import AutoTokenizer
      from ..auto.tokenization_auto import AutoTokenizer

      _lowerCamelCase	:					Optional[int]   =		kwargs.pop("""config"""      ,   SCREAMING_SNAKE_CASE)

      if config is None:
         _lowerCamelCase	:					Dict   =		RagConfig.from_pretrained(SCREAMING_SNAKE_CASE)

      _lowerCamelCase	:					List[Any]   =		AutoTokenizer.from_pretrained(
          SCREAMING_SNAKE_CASE      ,   config=config.question_encoder      ,   subfolder="""question_encoder_tokenizer""")
      _lowerCamelCase	:					Tuple   =		AutoTokenizer.from_pretrained(
          SCREAMING_SNAKE_CASE      ,   config=config.generator      ,   subfolder="""generator_tokenizer""")
      return cls(question_encoder=SCREAMING_SNAKE_CASE      ,   generator=SCREAMING_SNAKE_CASE)





   def __call__(							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  str:
      return self.current_tokenizer(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  List[str]:
      return self.generator.batch_decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self      ,   *SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)  ->  List[Any]:
      return self.generator.decode(*SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE)





   def 		UpperCamelCase_  (							self)  ->  Optional[int]:
      _lowerCamelCase	:					List[str]   =		self.question_encoder





   def 		UpperCamelCase_  (							self)  ->  Tuple:
      _lowerCamelCase	:					List[str]   =		self.generator





   def 		UpperCamelCase_  (							self      ,   SCREAMING_SNAKE_CASE      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = "longest"      ,   SCREAMING_SNAKE_CASE = None      ,   SCREAMING_SNAKE_CASE = True      ,   **SCREAMING_SNAKE_CASE      ,   )  ->  BatchEncoding:
      warnings.warn(
          """`prepare_seq2seq_batch` is deprecated and will be removed in version 5 of 🤗 Transformers. Use the """
          """regular `__call__` method to prepare your inputs and the tokenizer under the `with_target_tokenizer` """
          """context manager to prepare your targets. See the documentation of your specific tokenizer for more """
          """details"""      ,   SCREAMING_SNAKE_CASE      ,   )
      if max_length is None:
         _lowerCamelCase	:					Union[str, Any]   =		self.current_tokenizer.model_max_length
      _lowerCamelCase	:					Dict   =		self(
          SCREAMING_SNAKE_CASE      ,   add_special_tokens=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   max_length=SCREAMING_SNAKE_CASE      ,   padding=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
      if tgt_texts is None:
         return model_inputs
      # Process tgt_texts
      if max_target_length is None:
         _lowerCamelCase	:					List[Any]   =		self.current_tokenizer.model_max_length
      _lowerCamelCase	:					int   =		self(
          text_target=SCREAMING_SNAKE_CASE      ,   add_special_tokens=SCREAMING_SNAKE_CASE      ,   return_tensors=SCREAMING_SNAKE_CASE      ,   padding=SCREAMING_SNAKE_CASE      ,   max_length=SCREAMING_SNAKE_CASE      ,   truncation=SCREAMING_SNAKE_CASE      ,   **SCREAMING_SNAKE_CASE      ,   )
      _lowerCamelCase	:					Dict   =		labels["""input_ids"""]
      return model_inputs



"""simple docstring"""


from collections import OrderedDict
from typing import TYPE_CHECKING, Any, Mapping, Optional, Union

from ...configuration_utils import PretrainedConfig
from ...onnx import OnnxConfig
from ...utils import logging


if TYPE_CHECKING:
      from ... import FeatureExtractionMixin, PreTrainedTokenizerBase, TensorType


SCREAMING_SNAKE_CASE			  =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE			  =  {
    """microsoft/deberta-v2-xlarge""": """https://huggingface.co/microsoft/deberta-v2-xlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xxlarge""": """https://huggingface.co/microsoft/deberta-v2-xxlarge/resolve/main/config.json""",
    """microsoft/deberta-v2-xlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xlarge-mnli/resolve/main/config.json"""
    ),
    """microsoft/deberta-v2-xxlarge-mnli""": (
        """https://huggingface.co/microsoft/deberta-v2-xxlarge-mnli/resolve/main/config.json"""
    ),
}



class 	__a	(   _lowerCAmelCase						):
       UpperCamelCase_		:       Any				=      '''deberta-v2'''






       def __init__(	self			:				Dict							,   UpperCAmelCase_			:				Optional[int]=128_100							,   UpperCAmelCase_			:				List[str]=1_536							,   UpperCAmelCase_			:				List[str]=24							,   UpperCAmelCase_			:				int=24							,   UpperCAmelCase_			:				int=6_144							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   UpperCAmelCase_			:				Tuple=0.1							,   UpperCAmelCase_			:				int=0.1							,   UpperCAmelCase_			:				Optional[int]=512							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				Dict=0.02							,   UpperCAmelCase_			:				Optional[Any]=1e-7							,   UpperCAmelCase_			:				str=False							,   UpperCAmelCase_			:				Tuple=-1							,   UpperCAmelCase_			:				Optional[int]=0							,   UpperCAmelCase_			:				List[str]=True							,   UpperCAmelCase_			:				Any=None							,   UpperCAmelCase_			:				Optional[Any]=0							,   UpperCAmelCase_			:				Optional[Any]="gelu"							,   **UpperCAmelCase_			:				Optional[int]							,   )->	int:







              """simple docstring"""
              super().__init__(**UpperCAmelCase_      )

              UpperCamelCase       			=		hidden_size
              UpperCamelCase       			=		num_hidden_layers
              UpperCamelCase       			=		num_attention_heads
              UpperCamelCase       			=		intermediate_size
              UpperCamelCase       			=		hidden_act
              UpperCamelCase       			=		hidden_dropout_prob
              UpperCamelCase       			=		attention_probs_dropout_prob
              UpperCamelCase       			=		max_position_embeddings
              UpperCamelCase       			=		type_vocab_size
              UpperCamelCase       			=		initializer_range
              UpperCamelCase       			=		relative_attention
              UpperCamelCase       			=		max_relative_positions
              UpperCamelCase       			=		pad_token_id
              UpperCamelCase       			=		position_biased_input

              # Backwards compatibility
              if type(UpperCAmelCase_      ) == str:
                     UpperCamelCase       			=		[x.strip() for x in pos_att_type.lower().split("|"      )]

              UpperCamelCase       			=		pos_att_type
              UpperCamelCase       			=		vocab_size
              UpperCamelCase       			=		layer_norm_eps

              UpperCamelCase       			=		kwargs.get("pooler_hidden_size"							,   UpperCAmelCase_      )
              UpperCamelCase       			=		pooler_dropout
              UpperCamelCase       			=		pooler_hidden_act





class 	__a	(   _lowerCAmelCase						):






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Optional[Any]      )->	Mapping[str, Mapping[int, str]]:







              """simple docstring"""
              if self.task == "multiple-choice":
                     UpperCamelCase       			=		{0: "batch", 1: "choice", 2: "sequence"}
              else:
                     UpperCamelCase       			=		{0: "batch", 1: "sequence"}
              if self._config.type_vocab_size > 0:
                     return OrderedDict(
                         [("input_ids", dynamic_axis), ("attention_mask", dynamic_axis), ("token_type_ids", dynamic_axis)]      )
              else:
                     return OrderedDict([("input_ids", dynamic_axis), ("attention_mask", dynamic_axis)]      )






       @property
       def 						_SCREAMING_SNAKE_CASE							(	self			:				Union[str, Any]      )->	int:







              """simple docstring"""
              return 12






       def 						_SCREAMING_SNAKE_CASE							(	self			:				List[str]							,   UpperCAmelCase_			:				Union["PreTrainedTokenizerBase", "FeatureExtractionMixin"]							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				int = -1							,   UpperCAmelCase_			:				bool = False							,   UpperCAmelCase_			:				Optional["TensorType"] = None							,   UpperCAmelCase_			:				int = 3							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				int = 40							,   UpperCAmelCase_			:				"PreTrainedTokenizerBase" = None							,   )->	Mapping[str, Any]:







              """simple docstring"""
              UpperCamelCase       			=		super().generate_dummy_inputs(preprocessor=UpperCAmelCase_							,   framework=UpperCAmelCase_      )
              if self._config.type_vocab_size == 0 and "token_type_ids" in dummy_inputs:
                     del dummy_inputs["token_type_ids"]
              return dummy_inputs





